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      mxnet开车教程series1-mnist上手
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        <p>mxnet入门中文教程，让我们从mnist果蝇数据集开始开车<br><a id="more"></a></p>
<blockquote>
<p>本文由中南大学较为牛逼的研究生金天同学原创，欢迎转载，但是请保留这段版权信息，如果你对文章有任何疑问，欢迎微信联系我：jintianiloveu。牛逼大神一一为你解答！</p>
</blockquote>
<h3 id="前言"><a href="#前言" class="headerlink" title="前言"></a>前言</h3><p>最近搭建起了深度学习环境，mxnet被亚马逊钦定为官方的机器学习库，加上mxnet快速，代码清晰的特点，我赶紧乘上了mxnet快车，准备以mxnet为基础开始一些理论研究和产品实现。然而….mxnet搭建过程还是有点麻烦的，尤其是对于对编译过程不是非常熟悉的同学，这一点和caffe有点像，不过这不是问题，在本博客前面几篇文章对此有一个专门的教程，大家可以去看看，欢迎评论转载。这篇文章是mxnet开车教程的第一弹，让我们从果蝇数据集开始下手。</p>
<h3 id="开车！"><a href="#开车！" class="headerlink" title="开车！"></a>开车！</h3><p>二话不多说，开始开车，作为一名深度学习老司机，我们应该要学会果蝇数据集的正确下载方式，我这里就不贴了，去Lecun的官网下载。下载之后解压，你将会看到四个文件：<br><figure class="highlight stylus"><table><tr><td class="code"><pre><div class="line">t10k-images<span class="selector-class">.idx3-ubyte</span></div><div class="line">t10k-labels<span class="selector-class">.idx1-ubyte</span></div><div class="line">train-images<span class="selector-class">.idx3-ubyte</span></div><div class="line">train-labels.idx1-ubyte</div></pre></td></tr></table></figure></p>
<p>这就是训练集和测试集的数据和标签，很多人一看不知道这是什么鬼，其实我也不知道这是什么鬼，反正是一种文件格式就对了。不多说了直接上代码，开车之前先导入包：<br><figure class="highlight elm"><table><tr><td class="code"><pre><div class="line"><span class="keyword">import</span> struct</div><div class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</div><div class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</div><div class="line"><span class="keyword">import</span> mxnet <span class="keyword">as</span> mx</div><div class="line"><span class="keyword">import</span> logging</div><div class="line"><span class="title">logging</span>.getLogger().setLevel(logging.<span class="type">DEBUG</span>)</div></pre></td></tr></table></figure></p>
<p>哪个包缺少安装哪个，玩mxnet你不要告诉我你还没有安装mxnet，快去我的另外一片博文看教程安装。</p>
<h3 id="读取mnist数据集的正确姿势"><a href="#读取mnist数据集的正确姿势" class="headerlink" title="读取mnist数据集的正确姿势"></a>读取mnist数据集的正确姿势</h3><p>接下来我有必要传授大家读取mnist数据集的正确方式了，网上流传的各种方法都是瞎扯淡，不懂得科学内涵（手动装逼）。正确的读取方式我谢了两个函数，一个读取label，一个读取image：<br><figure class="highlight python"><table><tr><td class="code"><pre><div class="line"><span class="function"><span class="keyword">def</span> <span class="title">read_mnist_label</span><span class="params">(file_name)</span>:</span></div><div class="line">    bin_file = open(file_name, <span class="string">'rb'</span>)</div><div class="line">    magic, num = struct.unpack(<span class="string">"&gt;II"</span>, bin_file.read(<span class="number">8</span>))</div><div class="line">    label = np.fromstring(bin_file.read(), dtype=np.int8)</div><div class="line">    <span class="keyword">return</span> label</div><div class="line"></div><div class="line"><span class="function"><span class="keyword">def</span> <span class="title">read_mnist_image</span><span class="params">(file_name)</span>:</span></div><div class="line">    bin_file = open(file_name, <span class="string">'rb'</span>)</div><div class="line">    magic, num, rows, cols = struct.unpack(<span class="string">"&gt;IIII"</span>, bin_file.read(<span class="number">16</span>))</div><div class="line">    image = np.fromstring(bin_file.read(), dtype=np.uint8).reshape(num, rows, cols)</div><div class="line">    <span class="keyword">return</span> image</div></pre></td></tr></table></figure></p>
<p>将我们下载的文件传进去，就能得到label，images的输出，应该都是numpy.array的格式。</p>
<h3 id="测试图片"><a href="#测试图片" class="headerlink" title="测试图片"></a>测试图片</h3><p>我们写个显示图片的函数把：<br><figure class="highlight python"><table><tr><td class="code"><pre><div class="line"><span class="function"><span class="keyword">def</span> <span class="title">plot_image</span><span class="params">(image_array)</span>:</span></div><div class="line">    plt.imshow(image_array, cmap=<span class="string">'gray'</span>)</div><div class="line">    plt.show()</div></pre></td></tr></table></figure></p>
<p>输入图片矩阵，画出图片。<br><figure class="highlight lisp"><table><tr><td class="code"><pre><div class="line">val_img = read_mnist_image('t10k-images.idx3-ubyte')</div><div class="line">plot_image(<span class="name">val_img</span>[<span class="number">0</span>])</div></pre></td></tr></table></figure></p>
<p>这就把测试集的第一张图片显示出来了。</p>
<h3 id="搭建mxnet网络"><a href="#搭建mxnet网络" class="headerlink" title="搭建mxnet网络"></a>搭建mxnet网络</h3><p>这部分直接根据官网的来：<br><figure class="highlight nix"><table><tr><td class="code"><pre><div class="line"><span class="attr">batch_size</span> = <span class="number">100</span></div><div class="line"><span class="attr">train_iter</span> = mx.io.NDArrayIter(to4d(train_img), train_lbl, batch_size, <span class="attr">shuffle=True)</span></div><div class="line"></div><div class="line"><span class="attr">val_iter</span> = mx.io.NDArrayIter(to4d(val_img), val_lbl, batch_size)</div><div class="line"></div><div class="line"><span class="attr">data</span> = mx.sym.Variable('data')</div><div class="line"><span class="attr">data</span> = mx.sym.Flatten(<span class="attr">data=data)</span></div><div class="line"></div><div class="line"><span class="attr">fc1</span> = mx.sym.FullyConnected(<span class="attr">data=data,</span> <span class="attr">name='fc1',</span> <span class="attr">num_hidden=128)</span></div><div class="line"><span class="attr">act1</span> = mx.sym.Activation(<span class="attr">data=fc1,</span> <span class="attr">name='relu1',</span> <span class="attr">act_type="relu")</span></div><div class="line"></div><div class="line"><span class="attr">fc2</span> = mx.sym.FullyConnected(<span class="attr">data=act1,</span> <span class="attr">name='fc2',</span> <span class="attr">num_hidden=64)</span></div><div class="line"><span class="attr">act2</span> = mx.sym.Activation(<span class="attr">data=fc2,</span> <span class="attr">name='relu2',</span> <span class="attr">act_type="relu")</span></div><div class="line"></div><div class="line"><span class="attr">fc3</span> = mx.sym.FullyConnected(<span class="attr">data=act2,</span> <span class="attr">name='fc3',</span> <span class="attr">num_hidden=10)</span></div><div class="line"><span class="attr">mlp</span> = mx.sym.SoftmaxOutput(<span class="attr">data=fc3,</span> <span class="attr">name='softmax')</span></div><div class="line"></div><div class="line"><span class="attr">shape</span> = &#123;<span class="string">"data"</span>: (batch_size, <span class="number">1</span>, <span class="number">28</span>, <span class="number">28</span>)&#125;</div><div class="line"><span class="comment"># mx.viz.plot_network(symbol=mlp, shape=shape)</span></div><div class="line"></div><div class="line"><span class="attr">model</span> = mx.model.FeedForward(</div><div class="line">    <span class="attr">ctx=mx.gpu(0),</span></div><div class="line">    <span class="attr">symbol=mlp,</span></div><div class="line">    <span class="attr">num_epoch=4,</span></div><div class="line">    <span class="attr">learning_rate=0.1</span></div><div class="line">)</div><div class="line">model.fit(</div><div class="line">    <span class="attr">X=train_iter,</span></div><div class="line">    <span class="attr">eval_data=val_iter,</span></div><div class="line">    <span class="attr">batch_end_callback=mx.callback.Speedometer(batch_size,</span> <span class="number">200</span>)</div><div class="line">)</div></pre></td></tr></table></figure></p>
<h3 id="预测"><a href="#预测" class="headerlink" title="预测"></a>预测</h3><p>最后最重要的部分来了，那就是预测：<br><figure class="highlight stata"><table><tr><td class="code"><pre><div class="line">predict_img = val_img[0].astype(np.float32).<span class="keyword">reshape</span>((1, 1, 28, 28))/255.0</div><div class="line"><span class="keyword">prob</span> = model.<span class="keyword">predict</span>(predict_img)[0]</div><div class="line"><span class="keyword">print</span>('<span class="keyword">prob</span>:', <span class="keyword">prob</span>)</div><div class="line"><span class="keyword">print</span>('Classified <span class="keyword">as</span> &#123;0&#125; with probability &#123;1&#125;'.<span class="keyword">format</span>(<span class="keyword">prob</span>.argmax(), <span class="built_in">max</span>(<span class="keyword">prob</span>)))</div></pre></td></tr></table></figure></p>
<p>输出结果如下：<br><figure class="highlight actionscript"><table><tr><td class="code"><pre><div class="line">Classified <span class="keyword">as</span> <span class="number">7</span> <span class="keyword">with</span> probability <span class="number">0.9959895014762878</span></div></pre></td></tr></table></figure></p>
<p>说明我们的预测准确度还是非常高的啊！</p>

      
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        <ol class="nav"><li class="nav-item nav-level-3"><a class="nav-link" href="#前言"><span class="nav-number">1.</span> <span class="nav-text">前言</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#开车！"><span class="nav-number">2.</span> <span class="nav-text">开车！</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#读取mnist数据集的正确姿势"><span class="nav-number">3.</span> <span class="nav-text">读取mnist数据集的正确姿势</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#测试图片"><span class="nav-number">4.</span> <span class="nav-text">测试图片</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#搭建mxnet网络"><span class="nav-number">5.</span> <span class="nav-text">搭建mxnet网络</span></a></li><li class="nav-item nav-level-3"><a class="nav-link" href="#预测"><span class="nav-number">6.</span> <span class="nav-text">预测</span></a></li></ol>
    
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